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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0.97256194]\n[[ 0.00758958 -0.00807999 -0.01727656]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "data = pd.read_excel(\"劳动.xls\")                 #导入数据集\n",
    "x_train = data[[\"数字化程度\",\"数字化程度（平方）\",\"平均工资\"]]\n",
    "y_train = data[[\"就业率\"]]\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler   #数据中心化\n",
    "scaler=MinMaxScaler()\n",
    "x_train_scaled=scaler.fit_transform(x_train)\n",
    "\n",
    "def c_vector(x):                                 #提取第x列的数值，将所有特征归为一个向量，为一个个体\n",
    "    c = []\n",
    "    for i in range(len(x_train_scaled)):\n",
    "        c.append(x_train_scaled[i][x-1])\n",
    "    return np.array(c)\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt                 #绘制散点图\n",
    "\n",
    "x_1 = c_vector(1)\n",
    "x_2 = c_vector(2)\n",
    "x_3 = c_vector(3)\n",
    "y = data[[\"就业率\"]]\n",
    "\n",
    "fig ,ax = plt.subplots(1,3,figsize=(15,4))\n",
    "\n",
    "ax[0].scatter(x_1,y)\n",
    "ax[0].title.set_text('Degree of digitization')\n",
    "ax[1].scatter(x_2,y)\n",
    "ax[1].title.set_text(\"Square term\")\n",
    "ax[2].scatter(x_3,y)\n",
    "ax[2].title.set_text(\"average wage\")\n",
    "plt.show()\n",
    "\n",
    "from sklearn import linear_model                  #模型拟合\n",
    "\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(x_train_scaled[0:9],y_train[0:9])\n",
    "print(model.intercept_)\n",
    "print(model.coef_)\n",
    "y_pre = model.predict(x_train_scaled[-1].reshape(1,-1))              \n",
    "p_t = np.exp(y_pre)/(1+np.exp(y_pre))              #对2020年就业率作预测\n",
    "\n",
    "y_pre = model.predict(x_train_scaled)[0:9].reshape(1,9)[0]\n",
    "y_value = np.array(y_train)[0:9].reshape(1,-1)[0]\n",
    "sse = np.dot((y_pre-y_value),(y_pre-y_value))\n",
    "mse = np.sqrt(sse/(len(y_pre)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def t_test(model,x):          #传入的参数有整个模型，样本解释变量(最后给出每个系数的p值)\n",
    "    from scipy.stats import t \n",
    "    β = model.coef_[0]\n",
    "    n = len(x)\n",
    "    t_dist = t(n-1)\n",
    "    sigma_hat_square = sse/(n-1)\n",
    "    cov_β = np.linalg.inv((x.T@x))*sigma_hat_square\n",
    "    p_value = []\n",
    "    for i in range(len(β)):                                          #计算p值\n",
    "        t_value = (β[i]-0)/cov_β[i][i]\n",
    "        if t_value < 0:\n",
    "            p_value.append(2*t_dist.cdf(t_value))\n",
    "        else:\n",
    "            p_value.append(2*(1-t_dist.cdf(t_value)))\n",
    "    return p_value\n",
    "\n",
    "def R_square_and_adjusted_R_square(model,l_y,x):     #传入参数有整个模型，以及解释变量\n",
    "    n = len(l_y)\n",
    "    y_pre = model.predict(x)\n",
    "    sse = np.dot((y_pre[0]-l_y[0]),(y_pre[0]-l_y[0]))\n",
    "    sst = 0\n",
    "    y_bar = np.mean(l_y)\n",
    "    for i in range(len(l_y)):\n",
    "        sst += (l_y[i]-y_bar)**2\n",
    "    R_square = (sst-sse)/sst\n",
    "    adjust_R_square = 1 - (sse/sst)*n/(n-1)\n",
    "    return R_square,adjust_R_square\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[0.0, 2.730652343530665e-18, 8.22559861695409e-27]"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "t_test(model,x_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.9999947024804836, 0.9999940402905442)"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "R_square_and_adjusted_R_square(model,y_value,x_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        ],\n",
       "       [0.24453871, 0.09817492, 0.08942065],\n",
       "       [0.54381165, 0.34726461, 0.17423534],\n",
       "       [0.60180963, 0.4119539 , 0.26198273],\n",
       "       [1.        , 1.        , 0.36397985],\n",
       "       [1.        , 1.        , 0.46365599],\n",
       "       [0.77179304, 0.63225148, 0.58508456],\n",
       "       [0.95488804, 0.92075948, 0.73073048],\n",
       "       [0.99054684, 0.98312817, 0.87625045],\n",
       "       [0.98919198, 0.98072165, 1.        ]])"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "x_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}